As robots start to leave factories and begin to enter our schools,
workplaces, and homes, it is important that people are able to interact
with them in a way that is comfortable and natural to them. Eventually
this might be via natural language dialogue, but given the complexities
of language that may be not be available for a while. In the meantime,
another approach is to allow people to communicate with robots using
non-verbal communication, such as gestures and facial expressions. This
involves making robots able to both accurately sense what humans are
expressing (recognition) but also generating such expressions themselves
(synthesis).

Our research uses natural affect data to synthesize
realistic facial expressions and gestures on zoomorphic, humanoid, and
android robots. Then, to validate these expressions, we perform empirical
human-robot interaction experiments. We explore aspects of emotional
interaction such as empathy, rapport building, and cooperation.

However, expressions aren’t the whole story! In order to sustain
interaction with people, it is also important interactive robots express
the right thing at the right time. This is very difficult problem that
requires fundamental research into communication patterns in human-human
interaction, and then seeing if we can apply them to human-robot
interaction. To do this we are using techniques from the emerging field
of social signal processing.